Securing AI systems requires a different toolkit than traditional application security. You need tools that understand natural language attacks, can detect model-specific vulnerabilities, and operate at the speed of LLM inference. The good news: the market has matured significantly. The challenge: choosing the right combination for your needs.
This guide categorizes AI security tools by function and provides honest assessments of what each does well—and where it falls short.
Categories of AI Security Tools
AI security tools generally fall into six categories:
- AI Firewalls: Real-time filtering of inputs and outputs
- PII Protection: Detection and redaction of sensitive data
- Content Moderation: Policy enforcement on AI responses
- Observability: Monitoring and alerting for AI systems
- Red Teaming: Testing AI systems for vulnerabilities
- Governance Platforms: Comprehensive security + compliance
Most organizations need tools from multiple categories. Let's examine each.
1. AI Firewalls
These tools sit between users and AI systems, inspecting traffic for security threats.
Key Players
Lakera Guard
Specialized in prompt injection detection. Fast API with strong research backing. Narrower scope than full platforms.
Rebuff
Open-source prompt injection detection. Good for experimentation. Needs self-hosting and tuning.
Protect AI Guardian
ML model security focus. Strong on model-level attacks. Less focus on runtime protection.
Prime AI Guardrails
Comprehensive firewall with injection detection, PII filtering, and policy enforcement in one platform.
What to Look For
- Latency: Should add <50ms to requests
- Detection accuracy: Low false positive rate is critical
- Bypass resistance: Can it detect encoded or obfuscated attacks?
- Integration: How easily does it fit your architecture?
2. PII Protection Tools
Detecting and protecting personally identifiable information in AI interactions.
Key Players
Microsoft Presidio
Open-source PII detection with multiple language support. Production-tested, extensible. Requires integration work.
Amazon Comprehend PII
AWS native PII detection. Easy if you're in AWS. Limited customization, cloud only.
Private AI
Specialized PII redaction service. High accuracy, good compliance support. Adds API call overhead.
Prime AI Guardrails
Built-in PII detection with customizable entities. Integrated with other guardrails for unified policy.
What to Look For
- Entity coverage: Does it detect all PII types you care about?
- Custom entities: Can you add company-specific patterns?
- Accuracy: Precision vs. recall tradeoffs
- Processing location: Does data leave your environment?
3. Content Moderation
Ensuring AI outputs comply with content policies.
Key Players
OpenAI Moderation API
Free content classification. Easy to use. Limited categories, no customization.
Azure Content Safety
Multi-modal moderation (text + images). Azure integration. Content safety focus only.
Anthropic Constitutional AI
Built into Claude models. No separate integration. Model-specific, limited control.
Prime AI Guardrails
Customizable content policies with business-specific rules. Topic boundaries, competitor mention blocking, etc.
What to Look For
- Customization: Can you define your own policies?
- Granularity: Block vs. flag vs. modify responses
- Business rules: Support for domain-specific policies
- Appeal handling: What happens when content is incorrectly flagged?
4. AI Observability
Monitoring AI systems for security events and anomalies.
Key Players
LangSmith (LangChain)
Tracing and debugging for LangChain apps. Great developer experience. LangChain-centric.
Arize Phoenix
Open-source LLM observability. Evaluation and tracing. Requires setup and hosting.
Weights & Biases
ML experiment tracking extending to LLMs. Strong for model development. Less production focus.
Datadog LLM Observability
Enterprise APM extending to LLMs. Good if you're already on Datadog. Additional cost.
What to Look For
- Trace completeness: Full conversation capture with context
- Security alerts: Detection of anomalous patterns
- Integration: Works with your existing observability stack
- Retention: How long is data stored for investigation?
5. AI Red Teaming Tools
Testing AI systems for vulnerabilities before attackers find them.
Key Players
Garak
Open-source LLM vulnerability scanner. Comprehensive attack library. Requires security expertise to interpret.
Microsoft PyRIT
Red teaming framework from Microsoft. Well-documented. Focused on Microsoft scenarios.
Anthropic's Red Team
Professional services for Claude users. Expert-led. Anthropic models only.
Prompt Injection Libraries
Various GitHub repos with attack patterns. Free research. Not production tools.
What to Look For
- Attack coverage: Range of vulnerability types tested
- Actionable results: Clear remediation guidance
- Continuous testing: Ongoing vs. point-in-time
- Custom scenarios: Testing your specific use cases
6. Governance Platforms
Comprehensive solutions combining security, compliance, and governance.
Key Players
Prime AI Guardrails
Full-stack AI security and governance. Runtime protection, policy management, compliance controls. Best for enterprises with multiple AI systems.
IBM watsonx.governance
Enterprise governance suite. Strong on documentation and audit. Can be complex to deploy.
Arthur AI
Model monitoring and performance. Good for ML ops integration. Less runtime security focus.
Credo AI
AI governance with policy focus. Risk assessment and documentation. Less technical enforcement.
What to Look For
- Completeness: Does it cover all your security needs?
- Compliance: Built-in frameworks (SOC 2, HIPAA, EU AI Act)
- Scalability: Handles multiple AI systems and teams
- Time-to-value: How quickly can you deploy?
The Consolidation Advantage
While point solutions have their place, enterprises increasingly want consolidated platforms. Prime AI Guardrails combines AI firewall, PII protection, content moderation, and governance in a single platform—reducing integration complexity and providing unified policy management.
Building Your Security Stack
For Startups and Small Teams
- Start with OpenAI Moderation API (free)
- Add Presidio for PII (open source)
- Use LangSmith for observability
- Graduate to enterprise platform when you scale
For Mid-Size Companies
- Choose a platform that covers 80% of needs
- Add specialized tools for gaps
- Implement regular red team testing
- Build incident response procedures
For Enterprises
- Deploy comprehensive governance platform
- Integrate with existing security stack (SIEM, SOAR)
- Establish continuous security testing program
- Build dedicated AI security team or function
The Bottom Line
AI security tools have matured rapidly. You no longer need to build everything yourself—but you do need to choose wisely. The best approach matches your risk profile, technical capacity, and compliance requirements.
Start with your highest-priority threats (usually prompt injection and PII), then expand coverage as your AI deployment grows. And remember: tools are necessary but not sufficient. Security is ultimately about people and processes as much as technology.